agentleFS
sammyjoycesammyjoyce/prime-pstack/skills/swarm/SKILL.md

swarm

Skill1 starsChanged 2 months agoMITView on GitHub

Fan out N parallel workers, drain them, and return one report. Use for /skill:swarm, 'swarm this', or parallel coverage, races, gauntlets, and exploration.

91 other projects use a version of this, 49 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use poteto-help, make-bot-ui and 2 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ianclearySkill

    poteto-help

    Guides users through pstack setup, /poteto-mode, and picking the skill, playbook, or principle for a task. Type /poteto-help with a question.

    Also in 14 projects that use this

  • eryzerzSkill

    make-bot-ui

    Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.

    Also in 19 projects that use this

  • ianclearySkill

    principle-explain-the-number

    Apply before you trust, report, or act on a number you measured: a speedup, a regression, a throughput, a latency, or an eval result. Find what limits it, and rule out that it measured something other than the work you think.

    Also in 27 projects that use this

  • tristanphvnSkill

    ketchup-video

    Catch the user up like /keel:ketchup, with a short silent video of slides for what happened and the waiting calls as text under it. Use for /keel:ketchup-video.

    Also in 11 projects that use this

The file

We split this skill into the rules it gives an agent

5 rules. 0 are common, 3 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 87 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    When a worker must start from a non-default branch, tell it the branch and give it its own worktree (git worktree add).

    line 32
  2. Very specific:

    Every brief stands alone. Include the goal, scope, exact slice or race arm, how to verify, and what to report.

    line 34
  3. Distinctive:

    If a worker drops out, proceed with N-1 and note it.

    line 36
  4. Very specific:

    Read the replies (await rlm.collect(handles, timeout_ms=0) for status, the agent messages or report files for content).

    line 40
  5. Distinctive:

    Keep a compact result table, one-line evidenced issues, and explicit gaps or dropouts.

    line 42

“Common” is measured: kept by at least 6 in 10 of the 87 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Swarm

Fan out N parallel rlm.spawn workers. They may cover separate slices, race the same brief, or mix both. The parent waits, aggregates, and returns one report.

Start

Open a todo list in your reply with one entry per phase before launching anything.

  1. Frame
  2. Fan out
  3. Aggregate
  4. Report

Phase A: Frame

  1. State the done predicate and the artifact or report the swarm must return.
  2. Choose the shape. Partition into slices, race N workers on identical briefs, or mix both. For a race or mixed shape, declare first pass, rank all, or best-of before spawning.
  3. Set N from the user or derive it from the shape. N is total workers. Keep it under the parent inbox limit (20 pending replies) or have workers write reports to files.
  4. Pick the worker model from swarm workers in the injected pstack role table when present. Otherwise use zai/glm-5.3. For a model race, name each arm's model up front.
  5. Give each worker its own writable output when it writes.

Phase B: Fan out

Spawn all N workers in one ipython cell, one rlm.spawn(brief, name=<unique>, model=<configured or omitted>) each, then end the turn. Replies arrive as agent messages; a worker that writes a report to disk names the path in its reply.

When a worker must start from a non-default branch, tell it the branch and give it its own worktree (git worktree add).

Every brief stands alone. Include the goal, scope, exact slice or race arm, how to verify, and what to report. Reports use PASS, ISSUES, or BLOCKED with evidence.

If a worker drops out, proceed with N-1 and note it.

Phase C: Aggregate

Read the replies (await rlm.collect(handles, timeout_ms=0) for status, the agent messages or report files for content). For coverage, every required slice needs a result. For a race, apply the selection rule declared up front. Use first pass, rank all, or best-of. Do not paste raw worker dumps.

Keep a compact result table, one-line evidenced issues, and explicit gaps or dropouts.

Phase D: Report

Return one consolidated in-chat report with the table, issue one-liners, gaps or dropouts, and the race rule when used.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.